Wavelet-based enhancement of signal-averaged electrocardiograms for late potential detection

An optimal wavelet filter to improve the signal-to-noise ratio (SNR) of the signal-averaged electrocardiogram is described. As the averaging technique leads to the best unbiased estimator, the challenge is to attenuate the noise while preserving the low amplitude signals that are usually embedded in it. An optimal, in the mean-square sense, wavelet-based filter has been derived from the model of the signal. However, such a filter needs exact knowledge of the noise statistic and the noise-free signal. Hence, to implement such a filter, a method based on successive sub-averaging and wavelet filtering is proposed. Its performance was evaluated using simulated and real ECGs. An improvement in SNR of between 6 and 10 dB can be achieved compared to a classical averaging technique which uses an ensemble of 64 simulated ECG beats. Tests on real ECGs demonstrate the utility of the method as it has been shown that by using fewer beats in the filtered ensemble average, one can achieve the same noise reduction. Clinical use of this technique would reduce the ensemble needed for averaging while obtaining the same diagnostic result.